A NEW APPROACH TO DETAILED STRUCTURAL DECOMPOSITION FROM THE SPLASH AND PHAT SURVEYS: KICKED-UP DISK STARS IN THE ANDROMEDA GALAXY?
Bibliographic record
Abstract
We characterize the bulge, disk, and halo subcomponents in the Andromeda galaxy (M31) over the radial range 4 kpc < R proj < 225 kpc. The cospatial nature of these subcomponents renders them difficult to disentangle using surface brightness (SB) information alone, especially interior to ∼20 kpc. Our new decomposition technique combines information from the luminosity function (LF) of over 1.5 million bright (20 < m 814W < 22) stars from the Panchromatic Hubble Andromeda Treasury survey, radial velocities of over 5000 red giant branch stars in the same magnitude range from the Spectroscopic and Photometric Landscape of Andromeda's Stellar Halo survey, and integrated I -band SB profiles from various sources. We use an affine-invariant Markov chain Monte Carlo algorithm to fit an appropriate toy model to these three data sets. The bulge, disk, and halo SB profiles are modeled as a Sérsic, exponential, and cored power law, respectively, and the LFs are modeled as broken power laws. We present probability distributions for each of 32 parameters describing the SB profiles and LFs of the three subcomponents. We find that the number of stars with a disk-like LF is 5.2% ± 2.1% larger than the number with disk-like (dynamically cold) kinematics, suggesting that some stars born in the disk have been dynamically heated to the point that they are kinematically indistinguishable from halo members. This is the first kinematical evidence for a "kicked-up disk" halo population in M31. The fraction of kicked-up disk stars is consistent with that found in simulations. We also find evidence for a radially varying disk LF, consistent with a negative metallicity gradient in the stellar disk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".